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  "cells": [
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      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "from torchvision import datasets\n",
        "data_folder = '~/data/FMNIST' # This can be any directory you want to \n",
        "# download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0jVrDC71Gha"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix] \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HXarxIk1IVs"
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    return trn_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_MGNCCo1JvC"
      },
      "source": [
        "from torch.optim import SGD\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = SGD(model.parameters(), lr=1e-2)\n",
        "    return model, loss_fn, optimizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2wvP4rI1LYm"
      },
      "source": [
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train() # <- let's hold on to this until we reach dropout section\n",
        "    # call your model like any python function on your batch of inputs\n",
        "    prediction = model(x)\n",
        "    # compute loss\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    # based on the forward pass in `model(x)` compute all the gradients of     \n",
        "    # 'model.parameters()'\n",
        "    batch_loss.backward()\n",
        "    # apply new-weights = f(old-weights, old-weight-gradients) where \n",
        "    # \"f\" is the optimizer\n",
        "    optimizer.step()\n",
        "    # Flush gradients memory for next batch of calculations\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ECX-kaM1NWP"
      },
      "source": [
        "@torch.no_grad()\n",
        "def accuracy(x, y, model):\n",
        "    model.eval() # <- let's wait till we get to dropout section\n",
        "    # get the prediction matrix for a tensor of `x` images\n",
        "    prediction = model(x)\n",
        "    # compute if the location of maximum in each row coincides \n",
        "    # with ground truth\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71H93mxE1PLT"
      },
      "source": [
        "trn_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "9eL6egKc1QzM",
        "outputId": "4fd3dc60-d8f3-4b29-933f-2ab7aa7643ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "losses, accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    epoch_losses, epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        epoch_losses.append(batch_loss)\n",
        "    epoch_loss = np.array(epoch_losses).mean()\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        epoch_accuracies.extend(is_correct)\n",
        "    epoch_accuracy = np.mean(epoch_accuracies)\n",
        "    losses.append(epoch_loss)\n",
        "    accuracies.append(epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJi9ozS31cGp",
        "outputId": "b2091fac-a5e1-4075-aefe-d04a0cf0e879",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(121)\n",
        "plt.title('Loss value over increasing epochs')\n",
        "plt.plot(epochs, losses, label='Training Loss')\n",
        "plt.legend()\n",
        "plt.subplot(122)\n",
        "plt.title('Accuracy value over increasing epochs')\n",
        "plt.plot(epochs, accuracies, label='Training Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f209f1d2518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bOTcNqIU3IN8WvOaXMXhjypRmIl4iMzWkeTR4g/pei9eP/GmgrNH4H8XrG70R74rV0ebJftPRwXhNfNf5Mf3Vj7Mk1+BdOVkHvIXXz3tKyPx3/H3f4JxbFLKdt/z1TjKv2e0SvJZMp5xzLgevCe9deF3KFuINvl3SsoV4A9+lAWvxPpdn8K4QQdnHub4/bTtey6yteF38TtTP/O1twBvYcCJewlJSvLl4V7HuxWstlIvXyij0/9sX8frdb8C7cjfOf+9yvKuV/+fv56XApc65Q/5xuBSvj3oO3iCCw44j9jC8gQXX4R3r/pRdhBQRkepnFN7YeTl+S4MNzrkNeN2lr8NriVHiOcg59xreuDOv4J2L38Yb5Be8IsyleGPNXefPK8s/gDp458A5eN2jQl2P10r2W7yx+G4/MsM5tx+vS3tLvAs2pfJ/6Gfg3fAgNG+YgJcv5ANL/RhKW8cKvELMFLy7nRYfOPkevCEF5vh51RS81kwleQ4vN5iJl+scwBsc+ci2Dvr7dC4hhaqTyBF/qFKPfwkew8s5PzGz3XjHsqc/71jH+Xogyz9uY/C+OyfEOfch3s0tpuF/Dv6sEvM3vAJnTT+e7XjjRIV20/8SL3/egvd9/5Fz7sgg5mXl3o/gFaE+wStiPYv3HT+WdOBLM9uDdxx/7pxbcxzvEymRfberooiIHIuZ/RVo7pwbdcyFv//e6cBLzrlnTnlgIiIi1ZiZ/Q5o65wbccyFpVrxW0EtAWqVMV5iae+9AW8A7LNPR2wi5UEtfkREjsHM2vtNc83MeuA14X0r6LhERETE43cN+zHenZBEMLMrzKyWmTXCawn17okWfUSqChV+RESOLQqvifVevCbhfwf+G2hEIiIiAoCZ3YzXtfpD59zMoOORCuNWvC5pq/HGsFJXd6m21NVLRERERERERKSKUosfEREREREREZEqSoUfEREREREREZEqqkZ5bqxJkyYuOTm5PDcpIiIi5Wj+/PlbnHNNg45Dvks5mIiISNVWVg5WroWf5ORkMjMzy3OTIiIiUo7MLDvoGOT7lIOJiIhUbWXlYOrqJSIiIiIiIiJSRanwIyIiIiIiIiJSRanwIyIiIiIiIiJSRZXrGD8iIiI/REFBAXl5eRw4cCDoUKq92rVrEx8fT0RERNChiIiIVGvKj6qXk8nBVPgREZFKIy8vj6ioKJKTkzGzoMOptpxzbN26lby8PFq2bBl0OCIiItWa8qPq42RzMHX1EhGRSuPAgQM0btxYSU3AzIzGjRvryqKIiEgFoPyo+jjZHEyFHxERqVSU1FQM+hxEREQqDp2Xq4+T+axV+BERETlOW7duJS0tjbS0NJo3b05cXNzR14cOHSrzvZmZmYwbN+6Y2+jTp88piXX69Olccsklp2RdIiIiIqWpTPnREbfffjtxcXEUFRWd0vVWVBrjR0RE5Dg1btyYhQsXAnD//fdTr149fvGLXxydf/jwYWrUKPnU2r17d7p3737MbcyePfvUBCsiIiJSDipbflRUVMRbb71FQkICM2bMYODAgads3aHK2u/yViVa/Mxdu41Pl24MOgwREamGbrjhBsaMGUPPnj25++67mTt3Lr1796Zr16706dOH5cuXA99tgXP//fdz0003MWDAAFq1asXjjz9+dH316tU7uvyAAQP40Y9+RPv27bnuuutwzgHwwQcf0L59e7p168a4ceNOqGXPxIkT6dy5M506deKee+4BoLCwkBtuuIFOnTrRuXNnHn30UQAef/xxOnToQJcuXRg+fPgPP1hSpRQUFvHG/DxWbdoTdCgiIlLBVOT8aPr06XTs2JGxY8cyceLEo9M3btzIFVdcQWpqKqmpqUeLTRMmTKBLly6kpqZy/fXXH92/119/vcT4+vbty2WXXUaHDh0AuPzyy+nWrRsdO3bkqaeeOvqejz76iDPPPJPU1FQGDRpEUVERbdq0YfPmzYBXoGrduvXR1z9ExSg//UAPf7Kc3G376N+2KTVrVIlaloiIVCJ5eXnMnj2b8PBwdu3axaxZs6hRowZTpkzh3nvv5Y033vjee7799lumTZvG7t27adeuHWPHjv3ebTm/+uorvvnmG2JjYznrrLP44osv6N69O7feeiszZ86kZcuWXHPNNccd57p167jnnnuYP38+jRo1YvDgwbz99tskJCSQn5/PkiVLANixYwcADz30EGvXrqVWrVpHp4kcsefAYX779hIu7NycR65OCzocERGpYCpqfjRx4kSuueYahgwZwr333ktBQQERERGMGzeO/v3789Zbb1FYWMiePXv45ptveOCBB5g9ezZNmjRh27Ztx9zvBQsWsGTJkqN33XruueeIjo5m//79pKenc9VVV1FUVMTNN998NN5t27YRFhbGiBEjePnll7n99tuZMmUKqampNG3a9ASP/PdVicLP2AEp3Pifefx3YT5DuycEHY6IiJSDP7z7DUvX7Tql6+wQW5/fX9rxhN83dOhQwsPDAdi5cyejRo1i5cqVmBkFBQUlvufiiy+mVq1a1KpVi5iYGDZu3Eh8fPx3lunRo8fRaWlpaWRlZVGvXj1atWp1NJm45pprvnP1qCzz5s1jwIABRxOI6667jpkzZ3LfffexZs0abrvtNi6++GIGDx4MQJcuXbjuuuu4/PLLufzyy0/4uEjV1qhuTYb3SODFjGzuGtyOuIZ1gg5JRKTaU35Udn506NAhPvjgAx555BGioqLo2bMnH3/8MZdccglTp05lwoQJAISHh9OgQQMmTJjA0KFDadKkCQDR0dHH3O8ePXp851brjz/+OG+99RYAubm5rFy5ks2bN9OvX7+jyx1Z70033cSQIUO4/fbbee6557jxxhuPub3jUSWaxwxo25T2zaN4cuYaiopc0OGIiEg1U7du3aPP77vvPgYOHMiSJUt49913S73dZq1atY4+Dw8P5/Dhwye1zKnQqFEjFi1axIABAxg/fjyjR48G4P333+enP/0pCxYsID09/bRtXyqv0X1bAfDMrDUBRyIiIhVNRcyPPv74Y3bs2EHnzp1JTk7m888//053r+NVo0aNowNDFxUVfWcQ69D9nj59OlOmTCEjI4NFixbRtWvXMm/FnpCQQLNmzZg6dSpz587lwgsvPOHYSoz3lKwlYGbG2AEp/HzSQqYs28jgjs2DDklERE6zk7nyVB527txJXFwcAM8///wpX3+7du1Ys2YNWVlZJCcnM3ny5ON+b48ePRg3bhxbtmyhUaNGTJw4kdtuu40tW7ZQs2ZNrrrqKtq1a8eIESMoKioiNzeXgQMHcvbZZzNp0iT27NlDw4YNT/k+SeUV17AOl6XFMmluLuPOaUOjujWDDklEpFpTflR2fjRx4kSeeeaZo13B9u7dS8uWLdm3bx+DBg3iiSee4Pbbbz/a1eucc87hiiuu4M4776Rx48Zs27aN6OhokpOTmT9/PldffTXvvPNOqS2Ydu7cSaNGjYiMjOTbb79lzpw5APTq1Yuf/OQnrF279mhXryOtfkaPHs2IESO4/vrrj7aY+qGqRIsfgIs7tyAhug7/nr766OBOIiIi5e3uu+/m17/+NV27dj0tLWTq1KnDv//9by644AK6detGVFQUDRo0KHHZzz77jPj4+KOPrKwsHnroIQYOHEhqairdunVjyJAh5OfnM2DAANLS0hgxYgQPPvgghYWFjBgxgs6dO9O1a1fGjRunoo+UaEz/FPYXFPJCRlbQoYiISAVVEfKjffv28dFHH3HxxRcfnVa3bl3OPvts3n33XR577DGmTZtG586d6datG0uXLqVjx4785je/oX///qSmpnLnnXcCcPPNNzNjxgxSU1PJyMj4TiufUBdccAGHDx/mjDPO4Fe/+hW9evUCoGnTpjz11FNceeWVpKamMmzYsKPvueyyy9izZ88p6+YFYOVZJOnevbvLzMw8bet/MSOL+/77DZNu6UWvVo1P23ZERCQYy5Yt44wzzgg6jMDt2bOHevXq4Zzjpz/9KW3atOGOO+4o9zhK+jzMbL5z7tj3ZZVydbpzsNEvzGN+9na++NU5RNasEg3KRUQqDeVHnoqSH/1QmZmZ3HHHHcyaNavUZU40B6syLX4AhnZPoHHdmoyfsTroUERERE6bp59+mrS0NDp27MjOnTu59dZbgw5JqrmxA1LYvq+AyfNygw5FRESqqaqQHz300ENcddVVPPjgg6d0vVWqxQ/Av6at4v99vJwPxvWlQ2z907otEREpX7qiVbGoxU/lUR452NDxs1m34wDTfzmAiPAqdW1RRKRCU35U/VTrFj8AI3olUa9WDbX6ERERESlHY/qnkL9jP+8uWhd0KCIiIhKiyhV+GtSJ4Nqeiby3eB05W/cFHY6IiJxiGsC/YtDnIMUNbBdDu2ZRPDljjb4fIiLlTP/vVh8n81lXucIPwI/PbkmNsDCemqVWPyIiVUnt2rXZunWrkpuAOefYunUrtWvXDjoUqUDCwoxb+7di+cbdTFu+KehwRESqDeVH1cfJ5mBV8rYLzerX5soz43gtM4+fD2pL06haQYckIiKnQHx8PHl5eWzevDnoUKq92rVrEx8fH3QYUsFcmhrL3z9ZwRPTV3NO+2ZBhyMiUi0oP6peTiYHq5KFH4Bb+rVicmYuz89eyy/Pbx90OCIicgpERETQsmXLoMMQkVJEhIcxum9L/vDuUjKzttE9OTrokEREqjzlR3IsVbKrF0CrpvW4sFNzJmRks/tAQdDhiIiIiFQLw9ITaBQZoRttiIiIVBDHLPyYWW0zm2tmi8zsGzP7gz+9pZl9aWarzGyymdU8/eGemDH9U9h94DCvfJkTdCgiIiIi1UJkzRqM6pPMlGWbWLFxd9DhiIiIVHvH0+LnIHCOcy4VSAMuMLNewF+BR51zrYHtwI9PX5gnp0t8Q85q3ZhnP1/LwcOFQYcjIiIiAoCZPWdmm8xsSbHpt5nZt/7Ftr/5084ys8VmlmlmbfxpDc3sEzOrkK23R/VOpk5EuFr9iIiIVADHTBacZ4//MsJ/OOAc4HV/+gvA5aclwh9obP/WbNp9kLcW5AcdioiIiMgRzwMXhE4ws4HAECDVOdcReNifdRdwEXA7MMaf9lvgL865onKJ9gQ1qluT4T0SeGfhOvJ37A86HBERkWrtuK4SmVm4mS0ENgGfAquBHc65w/4ieUDc6QnxhzmrdWM6xzXgyZlrKCzS7e1EREQkeM65mcC2YpPHAg855w76yxy5J3oBEOk/CswsBUhwzk0vp3BPyui+rQB4ZtaagCMRERGp3o6r8OOcK3TOpQHxQA/guG+TZWa3+E2TM4O4vZyZMXZACmu37OXjbzaU+/ZFREREjlNboK8/huIMM0v3pz8ITAB+DfwT+DNei58yBZ2DxTWsw2VpsUyam8v2vYfKffsiIiLiOaF+4c65HcA0oDfQ0MyO3A4+HiixL5Vz7innXHfnXPemTZv+oGBP1vkdm9OySV2emL4a59TqR0RERCqkGkA00Av4JfCqmZlzbqFzrpdzbiDQClgPmH9zjZfMrFlJK6sIOdiY/insLyhkQkZ2INsXERGR47urV1Mza+g/rwOcByzDKwD9yF9sFPDf0xXkDxUeZtzSrxVf5+/ki1Vbgw5HREREpCR5wJv++IpzgSKgyZGZZmZ4LX3+BPweuBt4GhgXQKzHpW2zKM49I4bnZ69l36HDx36DiIiInHLH0+KnBTDNzBYD84BPnXPvAfcAd5rZKqAx8OzpC/OHu/LMOGKiaunuEiIiIlJRvQ0MBDCztkBNYEvI/JHAB865bXjj/RT5j8hyjvOEjOmfwvZ9Bbw6LzfoUERERKqlGsdawDm3GOhawvQ1eOP9VAq1aoTz47Nb8uCH37I4bwdd4hsGHZKIiIhUU2Y2ERgANDGzPLwWPM8Bz/m3eD8EjHJ+H3UziwRuAAb7q3gE+MBf7tpyDf4EdU+OJj25EU/PWst1vZKICK+Qd6AXERGpsqrVmffanolE1a6hVj8iIiISKOfcNc65Fs65COdcvHPuWefcIefcCOdcJ+fcmc65qSHL73PODXTOFfivZznnOjvnujnnlge3J8dnTP8U8nfs573F64IORUREpNqpVoWfqNoRXN8riQ+XbGDN5nm95o8AACAASURBVD1BhyMiIiJSLQxsF0O7ZlGMn75GN9oQEREpZ9Wq8ANw41ktiQgP46mZa4IORURERKRaCAszbu3fiuUbdzNt+aagwxEREalWql3hp2lULa7uHs+bC/LZuOtA0OGIiIiIVAuXpsYS17AOT0xXl3sREZHyVO0KPwC39E3hcFERz32+NuhQRERERKqFiPAwRvdtybys7czP3hZ0OCIiItVGtSz8JDaO5OIusbw0J5ud+wqCDkdERESkWhiWnkCjyAiemK4u9yIiIuWlWhZ+AMb0b8XeQ4W89GV20KGIiIiIVAuRNWswqk8yU5ZtZMXG3UGHIyIiUi1U28JPx9gG9G/blP98sZYDBYVBhyMiIiJSLYzqnUydiHCenKFWPyIiIuWh2hZ+AMYOSGHLnkO8Nj8v6FBEREREqoVGdWsyvEcC/12YT/6O/UGHIyIiUuVV68JPz5bRdE1syFMzV3O4sCjocERERESqhdF9WwHw7CzdaENEROR0q9aFHzNjTP8Ucrft5/2v1wcdjoiIiEi1ENewDpelxTJxbg7b9x4KOhwREZEqrVoXfgDOO6MZKU3rMn7GGpxzQYcjIiIiUi2M6Z/C/oJCJmToRhsiIiKnU7Uv/ISFea1+lq3fxYwVm4MOR0RERKRaaNssikHtY3h+9lr2HTocdDgiIiJVVrUv/AAMSYujRYPaPDF9ddChiIiIiFQbYweksH1fAa/Oyw06FBERkSpLhR+gZo0wRvdtxZdrtzE/e3vQ4YiIiIhUC92To+me1IinZ62lQDfaEBEROS1U+PENT0+gQZ0Ixs9Qqx8RERGR8jJ2QAr5O/bz3uJ1QYciIiJSJanw46tbqwaj+iTz6dKNrNy4O+hwRERERKqFge1iaNusHuOn60YbIiIip4MKPyFu6JNM7Ygwnpy5JuhQRERERKqFIzfaWL5xN9OWbwo6HBERkSpHhZ8Q0XVrMjw9kbe/ymfdjv1BhyMiIiJSLVyaGktcwzqMn66LbyIiIqeaCj/FjO7bEoBnZq0NOBIRERGR6iEiPIzRfVsyN2sb87O3BR2OiIhIlaLCTzHxjSK5LDWWiXNz2L73UNDhiIiIiFQLw9ITaBQZwRNq9SMiIlXM4cIiPvx6PZ+v3BLI9lX4KcGt/VPYX1DICxlZQYciIiIiUi1E1qzByN7JTFm2kRW60YaIiFQBW/Yc5J9TV9L3b9MY+/ICXpqTHUgcKvyUoF3zKM49I4YXZmex79DhoMMRERERqRZG9UmmTkQ4T85Qqx8REamcnHN8lbOdOyYvpM+DU3n4kxW0jqnH0yO786/rzgwkphqBbLUSGDsghaueyGDyvFxuPKtl0OGIiIiIVHnRdWsyLD2Bl+Zkc9fgtsQ2rBN0SCKVUkFhERHhusYvUp4OFBTy3uL1TMjIYnHeTurVqsG1PRMZ0SuJ1jH1Ao1NhZ9SdEuKpkdyNE/PXMOIXkn6j1NERESkHIzu25KX5mTzzKy1/O7SDkGHI1LpPP/FWv78wTLO69CM4emJnN26CWFhFnRYIlVW3vZ9vPxlDpPn5bJt7yFax9TjT0M6csWZ8dSrVTFKLqpmlGHMgFas23mAdxauCzoUERERqULM7Dkz22RmS0Km3W9m+Wa20H9c5E8/y8wWm1mmmbXxpzU0s0/MrMrlckdutDFpnm60IXKiNu8+yMOfrCAhOpKM1VsZ+dxc+v5tGo9/tpL1O/cHHZ5IleGcY/aqLdwyIZN+f5vGkzNWk57ciFdG9+TTO/pxfe/kClP0AbX4KdPAdjG0axbF+BmruaJrnCrlIiIicqo8D/wTmFBs+qPOuYeLTbsLuAhIBsb4r38L/MU5V3R6wwzGrf1TePOrfCZkZPPzc9sEHY5IpfHwx8s5UFDIMyO7E9eoDp98s5FJ83J45NMV/GPKCga0i2F4egID28eoR4PISdhz8DBvLcjjhYxsVm3aQ3Tdmozpn8J1vZKIq8Ddk1X4KYOZMXZACrdPXsjUbzdxbodmQYckIiIiVYBzbqaZJR/n4gVApP8oMLMUIME5N/30RBe8ds2jGNQ+hudnr+Xmfi2JrKmUVeRYluTv5NX5uYw+uyWtmnrjiVyaGsulqbHkbN3H5MwcXsvM45ZvN9E0qhZDu8UzLD2BpMZ1A45cpOJbvXkPL2Zk8/r8PPYcPEyX+Ab8fWgqF3dpQe2I8KDDOyadRY/hki4tePiT5fx7+ioGnRGDmVr9iIiIyGnzMzMbCWQCdznntgMP4rUM2g9cDzyM1+KnShs7IIUfjc/g1Xm53KAbbYiUyTnHH979hujImtw26Put5BIbR/LL89tzx7ltmbZ8M5Pm5jB+xmr+PX01fVIaM7xHIoM7NKsUP2BFykthkWPqt5uYkJHFrJVbqBkexsVdWjCqTzJpCQ2DDu+EqPBzDDXCw7ilXyt+999vmJe1nR4to4MOSURERKqmJ4A/Ac7/9+/ATc65hUAvADPrB6z3ntpkvNZAdznnNhZfmZndAtwCkJiYWC47cCp1T46me1Ijnp61lut0ow2RMr23eD3zsrbz4JWdqV87otTlaoSHcV6HZpzXoRkbdh7gtcxcJmfmMm7iVzSMjODKrvEM75FA22ZR5Ri9SMWyfe8hJmfm8mJGNvk79tO8fm1+Mbgtw9ITaRpVK+jwToo558ptY927d3eZmZnltr1TZf+hQs7661RS4xvwnxt7BB2OiIhIhWVm851z3YOOozLwu3q955zrdDzzzGt2/DEwHPg/4F68cX8GO+d+U9a2KmsONmXpRkZPyOTRYalc0TU+6HBEKqT9hwoZ9PfpNIysybu3nU34CY5LWlTk+GL1FibNzeWTpRsoKHScmdiQ4T0SuaRLC3W1lGpjSf5OXpidxTuL1nHwcBG9WkUzqncy53VoRo1KcPGhrBxMf8XHoU7NcG7sk8zfP13BsvW7OKNF/aBDEhERkSrGzFo459b7L68AlhRbZCTwgXNum5lFAkX+I7IcwyxX57SPoW2zejw5Yw2Xp8Wpy71ICZ6auYZ1Ow/w6LC0Ey76AISFGX3bNKVvm6Zs3XOQNxfkM3FeDne/vpg/vruUy9JiuSY9kU5x9fU3KFXOocNFfLhkPS/MzmJBzg7qRITzo27xjOydTLvmVafl2zELP2aWgNevvBle0+OnnHOPmdn9wM3AZn/Re51zH5yuQIM2sncy42es5skZq/nH8K5BhyMiIiKVmJlNBAYATcwsD/g9MMDM0vDyrSzg1pDlI4EbgMH+pEeAD4BDwLXlFXd5Cwszbu2Xwl2vLWL68s0MbB8TdEgiFcq6Hft5YsYqLu7cgp6tGv/g9TWuV4ub+7VidN+WZGZvZ+LcHN5ckMcrX+bQoUV9rumRwGVpcTSoU3p3MpHKYMPOA7zyZTavzM1ly56DtGxSl99d0oGrusVXye/3Mbt6mVkLoIVzboGZRQHzgcuBq4E9JdxytFSVtZnxEX9+fynPfZHF9F8MICG6yl5cExEROWnq6lUxVeYcrKCwiP5/m0Z8o0heHdM76HBEKpRxE7/i4282MOXO/qft98nO/QW8szCfiXNzWbp+F7Ujwriocwuu6ZFI96RGagUklYZzjrlrtzEhI5uPvtlAkXOc0y6GkX2S6du6CWEn0WKuIvlBXb38Jsfr/ee7zWwZEHdqQ6wcfnx2K56fncXTs9bwxyHf644vIiIiIqdYRHgYo/u24o/vLWV+9ja6JelGGyIAmVnbeGfROsad0/q0XpRuUCeC63snM6JXEkvydzFxXg7vLFzHmwvySWlal+HpiVx5ZhyN61XOQW+l6tt36DBvf7WOCRlZfLthNw3qRPDjs1syomcSiY2rR4OOExrc2R9ocCbQCbgTr8nxLr57y9FSVearTUfc/foi/rtwHV/86hya6D83ERGR71CLn4qpsudg+w4dps9DU+meFM0zo/T1Eikqcgz51xds3n2Qqb/oX+4DMO89eJj3v17PpLk5LMjZQUS4MbhDc4b3SOCslMrfckKqhqwte3lpTjavZuay68BhzmhRnxv6JHFZahx1aoYHHd4pd0oGdzazesAbwO3OuV1mVuItR0t4X6W+lWhxt/RL4bX5ebwwO4u7BrcLOhwRERGRKi+yZg1G9U7msc9WsnLjbtroVtNSzb2+II+v83fyj2Fpgdx1q26tGlzdPYGruyewYuNuJs3N5c2v8nj/6/XEN6rDsO4JDO2eQPMGtcs9NqneioocM1ZuZsLsLKav2Ey4GRd2bsGo3kl0q8ZdE4+rxY+ZRQDvAR875x4pYX4ypdyONFRlv9p0xJgX5zN79RZm/3oQ9WrpxmgiIiJHqMVPxVQVcrBtew9x1kNTuahzC/5+dWrQ4YgEZveBAgY+PIPE6Dq8MbZPhfkhe6CgkE+WbmTS3Bxmr95KmMHAdjEM75HIwHZNK8XtsKXy2rmvgNfm5/LinGyyt+6jaVQtru2RyHU9E4mpXz0KkD+oxY95/5M8CywLLfocxy1Hq6wxA1L46JsNTPwyh5v7tQo6HBEREZEqL7puTYalJ/DSnGzuGtyW2IZ1gg5JJBD/mraaLXsO8uyo7hWm6ANQOyKcy1JjuSw1lqwte3k1M5fX5ufx2YRMYqJqMbR7PMO6J1abMVWkfCxbv4sJGdm8/VU++wsK6Z7UiLsGt+OCjs2pWUPFxiOOp7nKWcD1wNdmttCfdi9wTWm3HK3q0hIa0ielMc98voaRfZKoVaPq9Q8UERERqWhG923JS3OyeWbWWn53aYegwxEpd1lb9vLc52v5Ubd4UhMaBh1OqZKb1OXuC9pzx3ltmfbtJibNy+WJ6av517TVnNW6McPTExncsZl+R8lJKSgs4pNvNvJCRhZz126jVo0wLk+L4/reSXSKaxB0eBXS8dzV63OgpFLyB6c+nMpjTP8URj43l7e/ymdYeuUfu0hERESkootvFMllqbFMmpfDbee0plHdmkGHJFKu/vzBMiLCjbvPrxxjjUaEhzG4Y3MGd2zO+p37eS0zj8nzcrlt4lc0iozgyjPjGZ6eoHG75Lhs2n2ASXNzefnLbDbuOkhCdB3uvag9V3dPoGGkzgdl0QA1J6lvmyZ0jK3PkzPX8KNuCYRr5HoRERGR0+7W/im8+VU+EzKy+fm5bYIOR6TcfL5yC58u3cjdF7SrlGOWtGhQh3GD2vDTga35YtUWJs3LYUJGFs9+vpZuSY0Ynp7AxV1aBDJYtVRczjkW5OxgQkYWH3y9noJCR7+2TfnLFUkMaBej3+HHSX9VJ8nMGDsghZ+98hWfLt3ABZ1aBB2SiIiISJXXrnkUg9rH8EJGFrf0a1Ulb8krUtzhwiL++N43JEZHctNZLYMO5wcJDzP6tW1Kv7ZN2bLnIG8uyGPS3Fx++fpi/vjuUi5Li+WaHonqslPNHSgo5J1F65iQkcWS/F1E1arBiF5JXN8riVZN6wUdXqWjws8PcGGnFiQ1Xs4T01dzfsfmFWpwNREREZGqasyAFIaOz+DVzFxG9UkOOhyR0+6VuTms2LiH8SO6UTui6hQ7m9SrxS39Uri5byvmZW1n0twcXp+fx8tf5tAxtj7DeyQyJC2W+rUjgg5Vyknutn289GU2k+flsmNfAW2b1eOByztxRdc46uqO2idNR+4HCA8zbu2Xwr1vfU3G6q30ad0k6JBEREREqrz05Gi6JzXiqZlruLZnIhG6TbRUYdv3HuLvn6ygT0pjzu/YLOhwTgszo0fLaHq0jOb3l3bkv4vymTg3l/veXsKf31/KxZ1juaZHAt2SGuliexXknOOLVVt5ISOLz5ZtxMwY3KEZI3sn06tVtD7zU0CFnx/oyjPjeOTTFTwxY7UKPyIiIiLlZEz/FEZPyOT9xeu5vGtc0OGInDb/mLKC3QcK+N2lHarFD+AGkRGM7J3M9b2SWJy3k0nzcnlnYT5vLMijdUw9hqcncOWZ8URrcPdKb/eBAt5ckM8LGVms2byXxnVr8pMBrbm2ZyKxDesEHV6VosLPD1Q7Ipwfn92Sv370LUvyd6ovqoiIiEg5OKd9DG2b1WP8jNUMSYutFj+IpfpZsXE3L32Zw3U9k2jfvH7Q4ZQrMyM1oSGpCQ357cVn8P7i9Uycl8MD7y/jrx99y+COzbkmPZE+KY0J0wC/lcqqTbuZkJHNG/Pz2HuokLSEhjw6LJWLOregVo2q05WxIlHh5xS4rlci/562iidmrOZf154ZdDgiIiIiVV6Y3+X+rtcWMX35Zga2jwk6JJFTyjnHn95bSt2a4dxxXtugwwlU3Vo1uDo9gavTE1i+YTeT5uXw5oJ83l+8noToOgzrnsDQ7gk0q4R3O6suDhcW8dm3m5iQkcUXq7ZSMzyMS1JbMKp3MqkJDYMOr8pT4ecUqF87ghG9k3hyxmrWbtlLyyZ1gw5JREREpMq7LC2Wv3/i3WhDhR+paqYs28SslVv4/aUd1K0pRLvmUfz+0o7cc0F7Pv5mA5Pm5vLwJyt4dMpKBraLYXh6AgPaNaWGxv6qELbtPcSkeTm8PCeH/B37iW1Qm1+e347h6Qk0rlcr6PCqDRV+TpEbz0rm2c/X8tTMNTx4ZeegwxERERGp8iLCwxjdtxV/fG8p87O30S0pOuiQRE6Jg4cLeeD9pbSOqceIXklBh1Mh1Y4IZ0haHEPS4li7ZS+vZubyWmYeU5ZtpFn9WgztlsCw9AQSoiODDrVaWpy3gxdmZ/Pu4nUcOlxEn5TG3HdJB849I0ZFuQCo8HOKxETV5kfd4nk9M487zm1DjJoZioiIiJx2w3sk8PjUlTwxfQ3PjFLhR6qG57/IInvrPl64qYfuWnccWjapyz0XtOfO89oy9dtNTJqbw7+nr+Kf01bRt00ThqUncF6HZho/5jQ7eLiQD75ezwuzs1mYu4PImuEM657AyN5JtGkWFXR41ZoKP6fQLX1bMWluDs99kcWvLmwfdDgiIiIiVV5kzRqM6p3MY5+tZOXG3fpxIZXept0H+L+pqxjUPob+bZsGHU6lEhEexvkdm3N+x+as27Gf1zLzeDUzl5+98hXRdWtyZdc4hvdIoHWM/p84ldbv3M/Lc3KYODeHrXsP0apJXe6/tANXdounfu2IoMMTVPg5pZKb1OWizi14eU42PxmYoi+5iIiISDkY1SeZJ2eu5smZa3h4aGrQ4Yj8IA9/vJyDhwv5zcVnBB1KpRbbsA4/P7cNPzunNZ+v2sKkuTk8PzuLZz5fS3pyI4alJ3Jx5xbUqalWQCfDOcecNduYkJHFJ0s3UuQcg9o3Y1SfJM5KaaI7rVUwKvycYmP6p/De4vW8NCebnwxoHXQ4IiIiIlVedN2aDE9P5KU52dx5XltiG9YJOiSRk/J13k5em5/HzX1b0appvaDDqRLCw4z+bZvSv21TNu8+yJsL8pg0L5dfvLaIP7zzDUO6xjI8PZFOcQ2CDrVS2HvwMG8vzGfC7GyWb9xNw8gIRvdtyYieSRpPqQJT4ecU6xTXgL5tmvDc51ncdFZLakeogiwiIiJyuo3u25IX52Tz7Odrue+SDkGHI3LCnHP84d1vaFy3Jj87RxeQT4emUbW4tX8Kt/RrxZdrtzF5njcg9EtzcugUV5/h6YkMSYslSj03vmftlr28mJHNa/Nz2X3gMB1j6/O3H3XhstRY/eatBFT4OQ3GDkjh2qe/5PX5eRqFX0RERKQcxDeKZEhqLBPn5nDbOa1pGKnbX0vl8u7i9WRmb+ehKztryIjTzMzo1aoxvVo15v5LO/L2wnwmzs3ht28v4c/vL+PiLi24pkcCZyY2wqz6dlkqLHLMWLGJF2ZnM2PFZiLCjYs6t2Bk72TOTGxYrY9NZaPCz2nQu1VjUhMa8tTMNQxPT9Dt6kRERETKwa39U3jzq3wmZGQzblCboMMROW77DxXy4AfL6Bhbn6HdE4IOp1ppEBnBqD7JjOydxKK8nUyel8M7C9fx+vw82sTUY1h6AleeGU903epTTN6x7xCvZebx4pxscrbto1n9Wtx5XluG90ggJkp3r66MVPg5DcyMsf1TGPPSfD5csoFLU2ODDklERESkymvXPIpB7WN4fnYWN/dtpUFbpdIYP2M163ce4LHhXQnXoLiBMDPSEhqSltCQ31zcgfcXr2Pi3FweeH8Zf/toOYM7NuOaHon0btW4yg5cvHTdLiZkZPH2wnwOFBTRIzmauy9ox/kdmxOhxgyVmgo/p8ngDs1o1bQuT0xfzSVdWqgZnIiIiEg5GDMghaHjM3g1M5dRfZKDDkfkmPJ37OfJmd5vhh4to4MOR4B6tWowLD2RYemJfLthF5Pm5vLWV/m8t3g9idGRDEtPYGi3eGLqV/7WLwWFRXy0ZAMTMrKYl7Wd2hFhXNE1jut7JdMhtn7Q4ckporLdaRIWZozpl8LS9buYuXJL0OGIiIhIBWJmz5nZJjNbEjLtfjPLN7OF/uMif/pZZrbYzDLNrI0/raGZfWJmyuWKSU+OpltSI56auYaCwqKgwxE5poc+/Bbn4NcX6fbtFVH75vW5/7KOfHnvIB4bnkZsw9r8v4+X0/uhqdw8IZPPlm3kcCX8v2bTrgP8Y8oKznpoKrdN/IqNuw7y24vP4Mtfn8uDV3ZR0aeKUYuf02hI11ge+XQFT0xfRf+2TYMOR0RERCqO54F/AhOKTX/UOfdwsWl3ARcBycAY//Vvgb845yrfr41yMLZ/CqMnZPL+4vVc3jUu6HBESjUvaxvvLlrHuEFtiGtYJ+hwpAy1I8IZkhbHkLQ41m7Zy+R5ubw+P49Pl26kef3aDO0ez9XdEyr0Lc2dcyzI2c7zs7P58Ov1HC5yDGjXlL/2TqZ/26ZVtgubqPBzWtWqEc7ovi154P1lfJWzna6JjYIOSURERCoA59xMM0s+zsULgEj/UWBmKUCCc2766Ymu8junfQxtYuoxfsZqhqTFqsu9VEhFRd7t21s0qM2Y/q2CDkdOQMsmdfnVhe25a3BbPlu2kUnzcvnntFX8c9oqzm7dhOHpiZzXoRk1a1SMRpkHCgp5Z+E6XsjI4pt1u4iqXYNRfZIZ0SuJlk3qBh2elAMVfk6z4T0S+b+pqxg/YzVPXt896HBERESkYvuZmY0EMoG7nHPbgQfxWgbtB64HHsZr8VMmM7sFuAUgMTHxtAVcEYWFGWP6p3DXa4uYvnwzA9vHBB2SyPe8Pj+PJfm7eGx4GpE19bOsMooID+OCTi24oFML8nfs57XMXF6dl8tPX1lA47o1uaqb1wqodUy9QOLL3baPl+ZkMzkzlx37CmjfPIq/XNGZy7vG6jtXzZhzrtw21r17d5eZmVlu26soHvlkOY9PXcWUO/vROiYq6HBEREROGzOb75zTlY7j4Lf4ec8518l/3QzYAjjgT0AL59xNxd7TD7gCeMJfpgCvQLSxrG1VxxysoLCI/n+bRnx0JK/e2jvocES+Y/eBAgY+PJ2kxnV5fUxvtUqrQgqLHDNXbmby3FymLNvI4SJHj+RohqUncFHnFqf9boNFRY7PV21hQkYWn327iTAzLujYnJG9k+jRMlrftSqsrBxMZb5yMKpPMk/NWsOTM9bw/4amBh2OiIiIVEChxRszexp4L3S+edn6b4HhwP8Bd+ON+zMO+E25BVpJRISHMbpvK/743lLmZ2+nW5K63EvF8c+pq9iy5xDP3ZCuH+JVTHiYMbBdDAPbxbBp9wHemJ/P5Hk53PXaIu5/9xuu6BrHsPQEOsY2OKXb3XWggDfm5/FiRjZrtuylSb2a3DawNdf0TKRFA40fVd2p8FMOGterxbDuCbwyN4c7B7fVH56IiIh8j5m1cM6t919eASwptshI4APn3DYziwSK/EfFHUk0YMN7JPD41JWMn7Gap0eqIZpUDGu37OW5L9YytFs8XeIbBh2OnEYxUbUZOyCFMf1bMWfNNibPy2HSvFwmZGTTJb4Bw9ITuCw1lqjaESe9jRUbdzMhI4s3F+Sz71AhXRMb8tjwNC7o1JxaNU5v6yKpPFT4KSej+7bipS9zeHbWWn57SYegwxEREZEAmdlEYADQxMzygN8DA8wsDa+rVxZwa8jykcANwGB/0iPAB8Ah4NryiruyiaxZg1G9k3nss5Ws3LibNs3U5V6C9+f3l1EzPIxfXtAu6FCknJgZvVMa0zulMffvO8RbX+UzaW4uv3lrCQ+8t4xLU1swLD2RMxMbHlcLsMOFRUxZtpEXZmeTsWYrNWuEcVlqLCN7J6mYKCVS4aecJERHcllqLK/MzeFn57SmYWTNoEMSERGRgDjnrilh8rNlLL8PGBjyehbQ+TSEVuWM6pPMkzNX8+TMNTysLvcSsJkrNjNl2UbuuaA9MVG1gw5HAtAwsiY3ntWSG/okszB3B5Pm5vLu4nW8mplH22b1GJ6eyBVd42hU9/u/F7fsOcjkebm8NCeb9TsPENewDvdc0J5h6QlEl7C8yBEq/JSjW/u34q2v8pmQkc24QW2CDkdERESkyouuW5Ph6Ym8NCebO89rS2xDdbmXYBwuLOJP7y0lqXEkN52dHHQ4EjAzo2tiI7omNuK+Szvw7qJ1TJqXyx/fW8pDH37LBZ2aMzw9gV6tGrM4fycTZmfx3uL1HCos4uzWTfjDZR0ZdEYzwsM0RpQcmwo/5ah98/qc0z6G52dncXPfVqd9RHcRERERgdF9W/LinGye/Xwt96nLvQTk5S9zWLlpD09d301jr8h31KtVg2t6JHJNj0SWrd/FpLk5vPVVPu8sWkfDyAh27Cugbs1wrumRwPW9k3SnaDlhKvyUs7EDUhg6PoNXM3MZ1Sc56HBEREREqrz4Rl6X+4lzc7hNXe4lANv3HuKRT1dwVuvGnNehWdDhSAV2Rov6/GFIJ3590Rl8uGQ9U5ZtomfLaK7oGveDBoGW6i0s6ACqm/TkaLonNeKpmWsoKCwKOhwRERGRauHW/q3Yd6iQCRnZQYci1dCjU1aw+0ABv7uko27fLseldkQ4V3SN51/X9O3CzwAAIABJREFUnsnI3skq+sgPosJPAMYOSCF/x37eW7wu6FBEREREqoXQLvf7DxUGHY5UI8s37OalOdmM6JVEu+bqoiMi5U+FnwAMbBdDu2ZRPDF9NUVFLuhwRETk/7d35/FV1Xf+x9+fm5WwhC1AgISQgCxuqICCYrAudUPbaWfELmIHyzLTaafj/Nppx+rUrtPfdPlN2wGxrrXajl1sVbRaK5siCoqKAZSwrwHCvmX7/P7IRUMMEEju/d7l9Xw87iP3nnPuOe8vJ9z7zed8zzkA0sL08WWqPlCj/128IXQUpAl3191PvaPOuVn68hVnhI4DIE2dtPBjZkVm9qKZVZjZO2b2pej07mb2vJm9F/3ZLfZxU0MkYppaXqp3t+3XiyurQscBAABIC6NKuusCTrlHHD1fsU0vrdqpL18xuMXbcwNAPLRmxE+dpNvdfbikiyT9o5kNl/Rvkl5w98GSXoi+RitNOLev+nXtoJlzK0NHAQAASBvTyxtPuX/6rS2hoyDFHamr17efXq7BvTrp0xcNCB0HQBo7aeHH3be4++vR5/skLZfUT9KNkh6KLvaQpI/FKmQqysqI6PPjBuq1tbv02trq0HEAAADSwkeG9tLgXp00c26l3DnlHrFz/4K1Wl99UHdOGK6sDK6wASCcU/oEMrMSSedJWiSpt7sfPVSyVVKL9yU0sylmttjMFm/fvr0NUVPPTaOK1b1jtmbOYdQPAABAPDSecl+mFVv3ac5K+qaIjap9h/Wzv76nK4b11rjBBaHjAEhzrS78mFknSb+T9M/uvrfpPG88XNLiIRN3n+XuI919ZEEBH3pNdcjO0K1jS/TCiiqt2Lr35G8AAABAm91wbl/1zc/VDE65R4z832dXqqa+Qf9+3bDQUQCgdYUfM8tSY9HnV+7+++jkbWZWGJ1fKImrFJ+GW8YMUF52hu6Zuzp0FAAAgLSQnRnR5HGlenVNtZas2xU6DlLMmxt26/ElG/X3Fw/UwJ4dQ8cBgFbd1csk3Sdpubv/qMmsP0maFH0+SdIf2z9e6uual62bRxfrT29u1sZdB0PHAQAASAsTRxWpa14WN9pAu2q8fXuFenbK1hc+Mih0HACQ1LoRPxdL+qykj5jZ0ujjWknfl3Slmb0n6Yroa5yG28YNVMSkX8xfEzoKAABAWuiYk6lbxpTo+YptWlW1L3QcpIg/vblZS9bt0lc+OlSdc7NCxwEASa27q9cCdzd3P8fdR0Qfs919p7tf7u6D3f0Kd+fWVKepML+DPjain3792nrt3H8kdBwAAIC0cOvYEuVmRTSTU+7RDg7W1On7z6zQWf266JMX9A8dBwDex30FE8TU8lIdqWvQQy+vDR0FAAAgLXTvmK2Jo4r1x6WbtHn3odBxkORmzl2tLXsO664JZyoSsdBxAOB9FH4SxKBenXXlsN56aOE6HThSFzoOAABAWrht3EA1uHTfAk65x+nbuOug7plbqQnn9tWoku6h4wDAMSj8JJBp48u051CtHnt1fegoAAAAaaF/tzzdcG5fPfbqeu0+WBM6DpLU959ZITPp364ZGjoKAHwIhZ8Ecn5xN11U2l2/mL9GNXUNoeMAAACkhanlpTpYU6+HF64LHQVJ6NU11XrqrS2aemmZ+nXtEDoOAHwIhZ8EM338IG3de1hPLN0UOgoAAEBaGNqniz4ytJcefHmtDtXUh46DJFLf4Prmk++oMD9X08rLQscBgBZR+Ekwlw7uqeGFXTRzbqUaGjx0HAAAgLQwrbxM1Qdq9PiSDaGjIIn8dskGvbN5r7527TB1yM4IHQcAWkThJ8GYmaaNL9Pq7Qf0XMW20HEAAADSwqiSbrpgQDfNmrdadfWcco+T23u4Vv/3zys1ckA3TTinMHQcADguCj8J6Nqz+qi4e55mzK2UO6N+AAAAYs3MNK28TBt3HdLTb28JHQdJ4Gd/XaWdB2p014QzZcbt2wEkLgo/CSgzI6Ipl5bqzQ279crq6tBxAAAA0sLlQ3tpcK9OmjGHg284sTU7DuiBl9boby/or7P754eOAwAnROEnQX3ygv7q2SlHM+ZWho4CAADamZndb2ZVZrasybT/MLNNZrY0+rg2Ov1iM3vLzBab2eDotK5m9pyZ0ZdrR5GIaWp5mVZs3ac5724PHQcJ7DtPVygnM0P/+tEhoaMAwEnRWUhQuVkZ+tzFJZr37nYt27QndBwAANC+HpR0dQvTf+zuI6KP2dFpt0u6VtI/S5oWnXaHpO+6OxejaWc3nNtXffNzNWMOB9/QsrnvbtdfllfpCx8ZpF6dc0PHAYCTovCTwD5z0QB1ysnUPfNWh44CAADakbvPk9Ta87lrJeVFH7VmViapyN3nxCheWsvOjGjyuFK9uqZaS9btCh0HCaa2vkHfeqpCA3rk6XMXl4SOAwCtQuEngeV3yNKnLyrW029t1rqdB0LHAQAAsfeF6Gld95tZt+i070l6WNLXJP1M0nfUOOIHMTJxVJHyO2RpJqfco5lHXlmnVVX7dcd1w5WTye3bASQHCj8JbvLFA5UZiWgWo34AAEh1MySVSRohaYukH0qSuy9194vc/TJJpdF5Zma/MbNHzKx3SyszsynR6wIt3r6d69Wcio45mZo0tkTPV2zTqqp9oeMgQVQfqNGPn39X4wb31BXDeoWOAwCtRuEnwfXqkqtPXNBfjy/ZqKp9h0PHAQAAMeLu29y9PnrdnnsljW463xrvF32HpG9JukvSV6LLffE465vl7iPdfWRBQUFsw6egW8eWKDcronvmcvANjX78/Ls6UFOvb1w/nNu3A0gqFH6SwJRLS1Vb36AHXlobOgoAAIgRMyts8vLjkpY1W+QWSbPdvVqN1/tpiD7y4pMwvXTvmK2Jo4r1xNJN2rLnUOg4CGzF1r361aJ1+syFxTqjd+fQcQDglFD4SQIDe3bUtWcV6pGF67T3cG3oOAAAoI3M7DFJCyUNMbONZjZZ0g/M7G0ze0vSZZK+3GT5PEm3Svp5dNKPJM2W9BNJM+OZPZ1MvmSgGly6b/6a0FEQkLvr7icr1KVDlr585Rmh4wDAKaPwkySmlZdp35E6PbpofegoAACgjdz9ZncvdPcsd+/v7ve5+2fd/Wx3P8fdb3D3LU2WP+jul7l7bfT1/OiyF7j7ynAtSW1F3fN0w7l99eir67X7YE3oOAjkuYpterlyp/7lyjPUNS87dBwAOGUUfpLE2f3zNW5wT923YI0O19aHjgMAAJAWppaX6mBNvX65cF3oKAjgcG29vvP0cp3Ru5M+Nbo4dBwAOC0UfpLI9PIybd93RL9/fVPoKAAAAGlhaJ8u+sjQXnrg5bU6VMPBt3Rz/0trtL76oO68/kxlZvCnE4DkxKdXEhlT1kPn9M/XrHmVqm/w0HEAAADSwrTyMlUfqNHjSzaEjoI4qtp7WD/76ypdOby3LhncM3QcADhtFH6SiJlpenmZ1u48qGeXbQ0dBwAAIC2MKumm84u7ata81aqrbwgdB3Hygz+vVF2969+vHRY6CgC0CYWfJHPVmX1U2rOjZsxdJXdG/QAAAMSamWn6+EHauOuQnn57y8nfgKT35obd+u2SjfrcJSUq6dkxdBwAaBMKP0kmI2KaWl6qZZv2asGqHaHjAAAApIXLh/bS4F6dNGNOJQffUpy76z+efEc9O+XoC5cNCh0HANqMwk8S+th5/dS7S45mzKkMHQUAACAtRCKmqeVlWrF1n+a8uz10HMTQH5du1hvrd+srVw9R59ys0HEAoM0o/CShnMwMTb5koF6u3Kk3N+wOHQcAACAt3HBuXxXm52omB99S1sGaOn3/mRU6u1++Pnl+/9BxAKBdUPhJUjePLlaX3EzNnEvHAwAAIB6yMyO6bVypFq2p1uvrd4WOgxiYOadSW/ce1l0ThisSsdBxAKBdUPhJUp1zs3TLmBI9+85WVW7fHzoOAABAWpg4qkj5HbIY9ZOCNlQf1D3zVuuGc/tqZEn30HEAoN1Q+Elit15couyMiGbNXR06CgAAQFromJOpSWNL9FzFNq2q2hc6DtrR959ZITPp364ZGjoKALQrCj9JrGenHN00qki/f2Ojtu45HDoOAABAWrh1bIlysyK6h4NvKWPR6p16+u0tmlZepr5dO4SOAwDtisJPkvv8uFI1uHTfAjoeAAAA8dC9Y7ZuGlmkJ5Zu0pY9h0LHQRvVN7i++WSF+ubnauqlZaHjAEC7o/CT5Iq65+n6cwr16KL12nOwNnQcAACAtHDb0YNv89eEjoI2+t/FG1SxZa++du0wdcjOCB0HANrdSQs/Zna/mVWZ2bIm0/7DzDaZ2dLo49rYxsSJTCsv04Gaev3ylbWhowAAAKSFou55mnBOoR57db12H6wJHQenae/hWv3Xn1dqVEk3XX9OYeg4ABATrRnx86Ckq1uY/mN3HxF9zG7fWDgVwwq76LIhBXrgpbU6VFMfOg4AAEBamDY+evBt4brQUXCafvrCe6o+WKO7JpwpM27fDiA1nbTw4+7zJFXHIQvaYPr4Qdp5oEaPL9kQOgoAAEBaGNqn8eDbgy9z8C0ZVW7frwdeWqu/u6BIZ/XLDx0HAGKmLdf4+YKZvRU9FaxbuyXCaRlV0k3nF3fVrHmrVVffEDoOAABAWuDgW/L6ztPLlZuVoX/96JDQUQAgpk638DNDUpmkEZK2SPrh8RY0sylmttjMFm/fvv00N4eTMTNNHz9IG3cd0tNvbwkdBwAAIC1w8C05zVlZpb+uqNIXLx+kgs45oeMAQEydVuHH3be5e727N0i6V9LoEyw7y91HuvvIgoKC082JVrh8aC8N7tVJM+ZUyt1DxwEAAEh5HHxLPrX1DfrWUxUq6ZGnW8cODB0HAGLutAo/Ztb0kvcfl7TseMsifiIR07TyMq3Yuk9zVjK6CgAAIB44+JZcfrlwnSq3H9Ad1w1XdmZbrnwBAMmhNbdzf0zSQklDzGyjmU2W9AMze9vM3pJ0maQvxzgnWumGEX3VNz9XM+ZUho4CAACQFiIR09SjB9/e5eBbIqs+UKOf/OVdjRvcU5cP6xU6DgDERWvu6nWzuxe6e5a793f3+9z9s+5+truf4+43uDvjWhNEVkZEt40r1atrq7VkHTdjAwAAiIcbzu2rwvxczeTgW0L70fMrdaCmXndeP5zbtwNIG4xtTEETRxepa16WZsxZHToKAABAWsjOjGjyJQO1aE21Xl+/K3QctGD5lr16dNF6ffaiARrcu3PoOAAQNxR+UlBedqZuHVuivyzfpne37QsdBwAAIC3cPLpY+R2yGPWTgNxddz9ZoS4dsvTPVwwOHQcA4orCT4qaNKZEHbIyNHMuHQ8AABKNmd1vZlVmtqzJtP8ws01mtjT6uDY6/WIze8vMFpvZ4Oi0rmb2nJnRl0sgHXMyNWnMAD1XsU2rqjj4lkj+/M5WLVy9U7dfeYa65mWHjgMAcUVnIUV165itiaOL9Kelm7Vp96HQcQAAwLEelHR1C9N/7O4joo/Z0Wm3S7pW0j9Lmhaddoek77p7Q8yT4pRMGlui3KyI7pnLKfeJ4nBtvb4ze7mG9O6sm0cXh44DAHFH4SeF3TauVJL0i/l0PAAASCTuPk9Sa+/CUCspL/qoNbMySUXuPidG8dAGPTrl6KaRRXpi6SZt2cPBt0Rw34I12lB9SHdOGK7MDP78AZB++ORLYf26dtCNI/rp169uUPWBmtBxAADAyX0helrX/WbWLTrte5IelvQ1ST+T9B01jvg5ITObEj09bPH27dxiPJ5uG1eqBpfum78mdJS0t23vYf38xVW6anhvXTyoZ+g4ABAEhZ8UN628VIdq6/XQy2tDRwEAACc2Q1KZpBGStkj6oSS5+1J3v8jdL5NUGp1nZvYbM3vEzHq3tDJ3n+XuI919ZEFBQZyaAEkq6p6nCecU6rFX12v3QQ6+hfSfz65QXb3r368bFjoKAARD4SfFDe7dWVcO762HFq7VgSN1oeMAAIDjcPdt7l4fvW7PvZJGN51vZqbGkT7fknSXpK9El/tivLPi5KaNL9OBmnr9cuG60FHS1tINu/X71zfp7y8ZqAE9OoaOAwDBUPhJA9PKy7T7YK1+/dqG0FEAAMBxmFlhk5cfl7Ss2SK3SJrt7tVqvN5PQ/SRF5+EOBVD+3TRZUMK9ODLa3W4tj50nLTT0OD6jz+9o4LOOfrCRwaFjgMAQVH4SQMXDOim0QO76775q1VTx80/AAAIzcwek7RQ0hAz22hmkyX9wMzeNrO3JF0m6ctNls+TdKukn0cn/UjSbEk/kTQzntnRetPKy7TzQI0eX8zBt3j745ubtHTDbn3lo0PUKSczdBwACIrCT5qYPr5Mm/cc1p/e3Bw6CgAAac/db3b3QnfPcvf+7n6fu3/W3c9293Pc/QZ339Jk+YPufpm710Zfz48ue4G7rwzXEpzI6IHddX5xV90zb7Xq6jn4Fi8HjtTp+8+s0Dn98/WJ8/uHjgMAwVH4SRPjzyjQ0D6dNXNupRoaPHQcAACAlGdmmlZepo27Dunpt7ec/A1oFzPnVmrb3iO6a8JwRSIWOg4ABEfhJ02YmaaPL9Oqqv36y/JtoeMAAACkhSuG9dagXp00c+5quXPwLdY2VB/UPfNW68YRfXXBgO6h4wBAQqDwk0auO7tQRd07aMbcSjoeAAAAcRCJmKZeWqrlW/Zq7rvbQ8dJed97ZrkyzPRv1wwNHQUAEgaFnzSSmRHRlHGlemP9br26pjp0HAAAgLRw44h+KszP1Yw5laGjpLRXVu/U7Le3avr4MhXmdwgdBwASBoWfNPO3I4vUo2O2Zsyl4wEAABAP2ZkRTb5koBatqdbr63eFjpOS6htc33yyQv26dtCUS0tDxwGAhELhJ83kZmXo7y8ZqDkrt6ti897QcQAAANLCzaOLld8hSzMZ9RMTv3ltg5Zv2auvXTtUuVkZoeMAQEKh8JOGPnPRAHXKydRMRv0AAADERcecTE0aM0DPL9+mVVX7Q8dJKXsO1eq/nlup0SXddd3ZhaHjAEDCofCThvI7ZOlTFxbrqbc2a/3Og6HjAAAApIVJY0uUkxnRrHkcfGtPP33hPe06WKM7JwyXGbdvB4DmKPykqcmXDFRmJKJ7568OHQUAACAt9OiUo5tGFukPb2zSlj2HQsdJCZXb9+vBl9fqppFFOqtffug4AJCQKPykqd5dcvU35/fT/y7eoO37joSOAwAAkBZuG1eqBpfuX7AmdJSU8O2nKtQhK0O3XzUkdBQASFgUftLYlEtLVVPfoAdfpuMBAAAQD0Xd8zThnEI9umi99hysDR0nqb24skovrtyuL14+WAWdc0LHAYCEReEnjZUWdNI1Z/XRwwvXad9hOh4AAADxMLW8TAdq6vXLV9aGjpK0ausb9K2nKjSwZ0dNGlsSOg4AJDQKP2luWnmZ9h2u06OL1oeOAgAAkBaGFXbRZUMK9MBLa3W4tj50nKT08MJ1Wr39gO64bpiyM/mTBgBOhE/JNHdO/666eFAP3bdgjY7U0fEAAACIh2nlZdp5oEaPL94QOkrS2bn/iH7yl3d16RkF+sjQXqHjAEDCo/ADTS8fpKp9R/SH1zeFjgIAAJAWRg/srvOLu+qeeatVV98QOk5S+eHz7+pgTb3uvH4Yt28HgFag8ANdPKiHzu6Xr3vmrVZ9g4eOAwAAkPLMTNPKy7Rx1yE9/faW0HGSRsXmvfr1q+v12YsGaFCvzqHjAEBSoPADmZmmjy/Tmh0H9Od3toaOAwAAkBauGNZbg3p10sy5q+XOwbeTcXfd/dQ7yu+QpS9fcUboOACQNCj8QJL00TP7aGDPjpo5t5KOBwAAQBxEIqapl5Zq+Za9mvvu9tBxEt6zy7bqldXV+perhig/Lyt0HABIGhR+IEnKiJimXFqqtzbu0cuVO0PHAQAASAs3juinwvxczZxbGTpKQjtcW6/vzF6uoX066+ZRRaHjAEBSofCD9/3N+f3Uq3OOZsyh4wEAABAP2ZkRTb5koF5ZXa031u8KHSdh3bdgjTbuOqQ7rx+uzAz+hAGAU8GnJt6Xk5mhyZcM1IJVO/TWxt2h4wAAAKSFm0cXK79DFqN+jmPrnsP6+Yur9NEze2vsoJ6h4wBA0jlp4cfM7jezKjNb1mRadzN73szei/7sFtuYiJdPXViszrmZdDwAAADipGNOpiaNGaDnKrZpVdX+0HESzg+eXaG6ete/Xzs8dBQASEqtGfHzoKSrm037N0kvuPtgSS9EXyMFdM7N0mcvGqBnlm3Vmh0HQscBAABIC5PGlignM6JZ8zj41tTr63fp929s0m3jBqq4R17oOACQlE5a+HH3eZKqm02+UdJD0ecPSfpYO+dCQJ+7eKCyMuh4AAAAxEuPTjm6aWSR/vDGJm3Zcyh0nITQ0OC6+8kKFXTO0T9cNih0HABIWqd7jZ/e7r4l+nyrpN7tlAcJoKBzjv5uZH/9bskmbdt7OHQcAABSTkun0jeZd7uZuZn1jL7+hJm9Y2bzzaxHdFqZmf0m3rkRW7eNK1WDS/cvWBM6SkJ4YukmLd2wW1+9eqg65WSGjgMASavNF3d2d5fkx5tvZlPMbLGZLd6+fXtbN4c4mTKuTHUNDXQ8AACIjQf14VPpZWZFkq6StL7J5H+SNErSPZI+FZ32bUl3xDYi4q2oe56uP6dQjy5arz0Ha0PHCerAkTp9/5kVOrd/vv7mvH6h4wBAUjvdws82MyuUpOjPquMt6O6z3H2ku48sKCg4zc0h3op75On6c/rqV4vWa8+h9O54AADQ3o5zKr0k/VjSV3TsQbUGSTmS8iTVmtk4SVvd/b2YB0XcTSsv04Gaev3ylbWhowT1P3NWqWrfEd054UxFIhY6DgAktdMt/PxJ0qTo80mS/tg+cZBIppaXav+ROj3yyrrQUQAASHlmdqOkTe7+ZrNZ35P0F0kTJD0m6RuSvhXneIiTYYVdNH5IgR54aa0O19aHjhPEhuqDunf+Gn1sRF9dMICbBwNAW7Xmdu6PSVooaYiZbTSzyZK+L+lKM3tP0hXR10gxZ/bNV/kZBXrgpTVp2/EAACAezCxP0tcl3dl8nrs/7+4XuPsENd5gY7akM8zst2Z2b/S9La2T0+2T1PTyMu08UKPHF28IHSWI785ergwzffWaoaGjAEBKaM1dvW5290J3z3L3/u5+n7vvdPfL3X2wu1/h7i0NVUYKmD6+TDv21+jxJRtDRwEAIJWVSRoo6U0zWyupv6TXzazP0QWiBZ5bJf1c0jfVOOp6gaRPt7RCTrdPXqMHdtd5xV01a/5q1dU3hI4TVy9X7tAzy7bqH8aXqTC/Q+g4AJAS2nxxZ6S2C492POZVpl3HAwCAeHH3t929l7uXuHuJpI2Sznf3rU0W+z+S/tvdayV1UON1gBrUeO0fpBAz0/TyMm2oPqSn395y8jekiPro7dv7de2gz19aGjoOAKQMCj84oXTteAAAEEvHOZX+RMv3lTTa3Z+ITvqppNckTZP0aEzDIogrhvXWoF6dNHPuajXeRDf1/fq19VqxdZ++fu0w5WZlhI4DACmDwg9OKh07HgAAxFJLp9I3m1/i7juavN7s7tc1ef24u5/p7he7OxfwSUGRiGnqpaVavmWv5r6b+rt4z8Fa/defV2r0wO669uw+J38DAKDVKPzgpNKt4wEAAJAIbhzRT4X5uZo5tzJ0lJj7fy+8p92HanXXhOEy4/btANCeKPygVY52PGbMSf2OBwAAQCLIzoxo8iUD9crqar2xflfoODGzqmq/Hl64VhNHFenMvvmh4wBAyqHwg1bJzozotnGlWrSmWkvWpW7HAwAAIJFMHF2s/A5ZKT3q59tPV6hDVoZuv2pI6CgAkJIo/KDVJo4qSvmOBwAAQCLplJOpW8YM0HMV27Sqan/oOO3uxRVVmrNyu750xWD17JQTOg4ApCQKP2i1jjmZmjS2RM9XbNOqqn2h4wAAAKSFW8eWKCczolnzUuvgW01dg771VIVKe3bULWNKQscBgJRF4Qen5NaxJcrNimjm3NWhowAAAKSFHp1y9Hcji/SHNzZp657DoeO0m4cXrtXqHQd0x/XDlJ3JnyUAECt8wuKUdO+YrYmjivXEG5u0efeh0HEAAADSwufHlarBpfsWpMbBtx37j+j/vfCeys8o0GVDeoWOAwApjcIPTtlt4wZKkn4xf03gJAAAAOmhqHuerj+nUI8uWq89B2tDx2mzHz73rg7V1Osb1w/j9u0AEGMUfnDK+nfL0w3n9tVjr67XrgM1oeMAAACkhWnlZTpQU69fvrI2dJQ2eWfzHv36tfW6ZUyJBvXqHDoOAKQ8Cj84LVPLy3Sotl4PL1wXOgoAAEBaGFbYReOHFOiBl9bqcG196Dinxd1195MV6tohS1+6fHDoOACQFij84LQM6dNZVwzrpQdfXqODNXWh4wAAAKSFaeVl2nmgRo8v2Rg6yml5ZtlWLVpTrduvGqL8vKzQcQAgLVD4wWmbPr5Muw7W6jevbQgdBQAAIC1cOLC7zivuqlnzKlVX3xA6zik5XFuv7zy9XEP7dNbNo4tDxwGAtEHhB6ftggHdNbqku34xf41qk6zjAQAAkIzMTNPKy7Sh+pBmL9saOs4puXfeam3afUh3ThiujAgXdAaAeKHwgzaZNr5Um3Yf0pNvbg4dBQAAIC1cOay3ygo6asacSrl76DitsnXPYf3PnEpdfWYfjS3rGToOAKQVCj9ok8uG9NKQ3p01c26lGhqSo+MBAACQzCIR09TyMi3fslfz3tsROk6r/OezK1Tvrq9fOyx0FABIOxR+0CZmpunjy/Tutv3664qq0HEAAADSwsdG9FOfLrmaMWdV6CgntWTdLv3hjU36/LiBKu6RFzoOAKQdCj9os+vPKVT/bh30P3NWJc1wYwAAgGSWnRnRbeMG6pXV1Xpj/a7QcY6rocF195PvqFfnHP3D+EGh4wBAWqLwgzZ43fPDAAAUh0lEQVTLzIhoyqWlen39br22NnE7HgAAAKlk4uhi5XfI0sy5laGjHNcf3tikNzfu0VevHqqOOZmh4wBAWqLwg3bxtxcUqXvH7ITueAAAAKSSTjmZumXMAD1XsU2V2/eHjvMh+4/U6T+fXaFzi7rq4+f1Cx0HANIWhR+0iw7ZGfrc2BL9dUWVlm/ZGzoOAABAWpg0tkTZGRHNmrs6dJQP+Z8XV6lq3xHdNWG4Ity+HQCCofCDdnPLmBJ1zM7QPYz6AQAAiIuenXJ006gi/f6Njdq653DoOO9bv/OgfjF/jT5+Xj+dX9wtdBwASGsUftBu8vOy9KkLi/XkW1u0ofpg6DgAAABp4fPjStXg0v0vrQkd5X3fnb1cGRHTV68eGjoKAKQ9Cj9oV5MvKVXEpHvnJ95wYwAAgFRU1D1P159TqF+9sk57DtaGjqOXV+3Qs+9s1T9eVqY++bmh4wBA2qPwg3bVJz9XHz+vn37z2gbt2H8kdBwAAIC0MPXSMh2oqdcji9YFzVFX36C7n6pQ/24ddNu40qBZAACNKPyg3U25tEw19Q166OW1oaMAAACkheF9u2j8kALdv2CNDtfWB8vx2GsbtGLrPn392mHKzcoIlgMA8AEKP2h3g3p10keH99FDL6/V/iN1oeMAAJBwzOx+M6sys2UtzLvdzNzMekZff8LM3jGz+WbWIzqtzMx+E+/cSGzTysu080CNHl+yMcj29xys1Y+eW6kLB3bXNWf1CZIBAPBhFH4QE9PGl2nv4To9tmh96CgAACSiByVd3XyimRVJukpS0y/Qf5I0StI9kj4VnfZtSXfENiKSzYUDu+u84q6aNa9SdfUNcd/+T154V3sO1erOCcNlxu3bASBRUPhBTIwo6qqxZT30iwWrdaQu3HBjAAASkbvPk1TdwqwfS/qKJG8yrUFSjqQ8SbVmNk7SVnd/L+ZBkVTMTNPKy7Sh+pBmL9sa122vqtqnhxeu08TRxTqzb35ctw0AODEKP4iZaeVl2rb3iP74xubQUQAASHhmdqOkTe7+ZrNZ35P0F0kTJD0m6RuSvtWK9U0xs8Vmtnj79u3tnheJ6cphvVVW0FEz51TK3U/+hnbg7rr7qeXKy87Q7VeeEZdtAgBar02FHzNba2Zvm9lSM1vcXqGQGsYN7qkz+3bRzHmVqm+IT8cDAIBkZGZ5kr4u6c7m89z9eXe/wN0nSLpR0mxJZ5jZb83s3uh7P8TdZ7n7SHcfWVBQENP8SByRiGlqeZkqtuzVvPd2xGWbL66s0rx3t+tLlw9Wj045cdkmAKD12mPEz2XuPsLdR7bDupBCzEzTx5dp9fYDer4ivsONAQBIMmWSBkp608zWSuov6XUze/8KudECz62Sfi7pm5ImSVog6dPxDovE9rER/dSnS65mzqmM+bZq6hr0raeWq7Sgo24ZUxLz7QEATh2neiGmrjmrUAN65GlGHIcbAwCQbNz9bXfv5e4l7l4iaaOk89296ZGT/yPpv929VlIHNV4HqEGN1/4B3pedGdFt4wZq4eqdWrphd0y39dDLa7VmxwF94/rhys7kTwsASERt/XR2Sc+Z2RIzm9IegZBaMiKmqZeW6c2Ne7SwcmfoOAAAJAQze0zSQklDzGyjmU0+yfJ9JY129yeik34q6TVJ0yQ9GtOwSEoTRxerS25mTEf97Nh/RP/9wnsaP6RAlw3pFbPtAADapq2Fn0vc/XxJ10j6RzO7tPkCXFgQf3N+P/XslKMZc2M/3BgAgGTg7je7e6G7Z7l7f3e/r9n8Enff0eT1Zne/rsnrx939THe/2N3pYOFDOuVkatLYEv25Yqsqt++PyTZ++NxKHaqt1x3XDY/J+gEA7aNNhR933xT9WSXpD5JGt7AMFxZMc7lZGZp8yUDNf2+Hlm3aEzoOAABAWpg0tkTZGRHNmru63de9bNMe/fq1DZo0tkSDenVq9/UDANrPaRd+zKyjmXU++lzSVZKWtVcwpJZPX1SszjmZjPoBAACIk56dcnTTqCL9/o2N2rrncLut191195MV6paXrS9ePrjd1gsAiI22jPjpLWmBmb0p6VVJT7v7s+0TC6mmS26WPjNmgJ55e4vW7DgQOg4AAEBa+Py4UjW4dP9La9ptnbPf3qpX11br9qvOUH6HrHZbLwAgNk678OPuq9393OjjTHf/TnsGQ+r53MUlysyIaNa89h9uDAAAgA8r6p6n684u1K9eWac9B2vbvL7DtfX67uzlGtqnsyaOKm6HhACAWOOei4ibXp1z9ckL+ut3Szaqam/7DTcGAADA8U0rL9OBmno9smhdm9c1a95qbdp9SHdNOFMZEWuHdACAWKPwg7iaMq5UdQ0Nuv+ltaGjAAAApIXhfbuo/IwCPfDSGh2urT/t9WzZc0gz5lTq2rP7aExZj3ZMCACIJQo/iKuSnh11bXS48d7DbR9uDAAAgJObPr5MO/bX6PElG097Hf/5zArVu+tr1wxrx2QAgFij8IO4m1Zepn1H6vTIK20fbgwAAICTu3Bgd40o6qp7561WXX3DKb9/ybpqPbF0s6aMK1VR97wYJAQAxAqFH8TdWf3yNW5wT92/YG2bhhsDAACgdcxM08eXaX31Qc1etvWU3tvQ4PrmkxXq3SVH08eXxSghACBWKPwgiMbhxkf0u9dPf7gxAAAAWu/KYb1VVtBRM+dUyt1b/b7fvb5Rb23co69ePVQdczJjmBAAEAsUfhDEmNIeOreoq+6Ze3rDjQEAAHBqIhHT1PIyVWzZq3nv7WjVe/YfqdMP/rxSI4q66mMj+sU4IQAgFij8IAgz0/TyxuHGz5zicGMAAACcno+N6Kc+XXI1c05lq5b/+YurtH3fEd01Ybgi3L4dAJIShR8Ec9Xw3iot6KgZpzjcGAAAAKcnOzOi28YN1MLVO7V0w+4TLrtu5wHdN3+N/ub8fjqvuFucEgIA2huFHwQTiZimXdo43Hh+K4cbAwAAoG0mji5Wl9zMk476+c7Ty5WZYfrq1UPjlAwAEAsUfhDUjef1VZ8uuZrRyuHGAAAAaJtOOZm6ZUyJ/lyxVZXb97e4zEurdui5im36x8sGqXeX3DgnBAC0Jwo/CConM+P94cZvrN8VOg4AAEBauPXiEmVnRDRr7uoPzaurb9DdT1aof7cOmnzJwADpAADticIPgps4ulj5HbI0cy6jfgAAAOKhZ6cc/d3IIv3+jY3auufwMfMee3W9Vm7bpzuuG6bcrIxACQEA7YXCD4LrlJOpSWMG6LmKbVpV1fJwYwAAALSvKZeWqsGl+19a8/603Qdr9MPn39VFpd310TP7BEwHAGgvFH6QECaNLVFOZkSz5jHqBwAAIB6KuufpurML9atX1mnPwVpJ0k/+8p72HqrVndefKTNu3w4AqYDCDxJCj045umlkkf7wxiZt2XModBwAAIC0MK28TAdq6vXIonV6b9s+/fKVdbp5dLGG9+0SOhoAoJ1khg4AHHXbuFI9smi97p23Rl+9Zkjct2+K/1GteB9IC3HczswUMXHUEACABDS8bxeVn1GgB15ao5dW7VDH7Az9y5VnhI4FAGhHFH6QMIq65+mGc/vq/pfWHHOuOVKLmRSxxjKbWWPBzezY50fnyxqLVZHI0eWb/IzOO3Z9jcWlSOQE63r/vUe32yTLMctbdPnmyxybvem2dMz6P3iu97M0a68d266m21IL/06RyAfrsmbbsubtavLv1uK67IM2fmiftLQu+6CNLe/XD2baMdPV4vQPved4yzXb6PHX3fK6Wvue4zz9cM7jretD2zx+npOt90Q5T/zveWyCE70nOzOi8UN6tRwMQNqZPr5ME2e9oh37d+ob1w9Xj045oSMBANoRhR8klK9dM1RD+nRWfYOHjhJT7vFvX4BNyqPbdbkaoi+OTmto8tzljT89+lPR+X40e+P7319OTZZtsv6jz9Vs/Uef6+iyDcdZ1zHb+mA/NbS0rQbJ1SCvb96Wpus7uq0W1tXCdr3Zto5d3qNtaWxI838nb5qh+bqibf8g5wfLIj316JitJd+4MnQMAAniwoHdNXJAN+05VKtbxgwIHQcA0M4o/CCh9OqSq2nlZaFjAGmjpWLbB4W4pgWnY6tEfsw6Wp7hTV40LzId+34/zvTm7zlm5af8Hj/ue46f85h1xaxtzf5tW9E2P9F7WpEzI8KplwA+YGZ68O9Hy92VlcElQAEg1VD4AYA0Zk1O4coIchUoID2Z2f2SrpdU5e5nNZt3u6T/klTg7jvM7BOS7pZULelj7r7TzMokfdfdb4p3dqSmTjn8WQAAqYqSPgAAQPw9KOnq5hPNrEjSVZLWN5n8T5JGSbpH0qei074t6Y7YRgQAAKmAwg8AAECcufs8NY7gae7Hkr6iY8/0a5CUIylPUq2ZjZO01d3fi3lQAACQ9BjTCQAAkADM7EZJm9z9TTv2Nm3fk/QXSZslfUbS45Imxj8hAABIRhR+AAAAAjOzPElfV+NpXsdw9+clPR9d7hZJsyWdYWb/KmmXpC+5+8EW1jlF0hRJKi4ujl14AACQ0DjVCwAAILwySQMlvWlmayX1l/S6mfU5ukC0OHSrpJ9L+qakSZIWSPp0Syt091nuPtLdRxYUFMQ2PQAASFiM+AEAAAjM3d+W1Ovo62jxZ6S772iy2P+R9N/uXmtmHdR4HaAGNV77BwAAoEWM+AEAAIgzM3tM0kJJQ8xso5lNPsnyfSWNdvcnopN+Kuk1SdMkPRrTsAAAIKkx4gcAACDO3P3mk8wvafZ6s6Trmrx+XI0XeQYAADghRvwAAAAAAACkKAo/AAAAAAAAKcrcPX4bM9suaV2MVt9T0o6TLpX80qGd6dBGiXamknRoo0Q7U0ks2zjA3bmFVIKhD9Zm6dBGiXamknRoo0Q7U0k6tFEK1AeLa+EnlsxssbuPDJ0j1tKhnenQRol2ppJ0aKNEO1NJOrQR8ZMOv0/p0EaJdqaSdGijRDtTSTq0UQrXTk71AgAAAAAASFEUfgAAAAAAAFJUKhV+ZoUOECfp0M50aKNEO1NJOrRRop2pJB3aiPhJh9+ndGijRDtTSTq0UaKdqSQd2igFamfKXOMHAAAAAAAAx0qlET8AAAAAAABoIqkKP2Z2v5lVmdmy48w3M/tvM1tlZm+Z2fnxztgeWtHO8Wa2x8yWRh93xjtjW5lZkZm9aGYVZvaOmX2phWWSfn+2sp1JvT/NLNfMXjWzN6Nt/GYLy+SY2W+i+3KRmZXEP2nbtLKdt5rZ9ib78rYQWdvKzDLM7A0ze6qFeUm/L486STtTZV+uNbO3o21Y3ML8pP+cRXzQB3t/flJ/Z0vp0QdLh/6XRB+s2TKp8r1NHyx19mVC9cEyY7nyGHhQ0s8kPXyc+ddIGhx9XChpRvRnsnlQJ26nJM139+vjEycm6iTd7u6vm1lnSUvM7Hl3r2iyTCrsz9a0U0ru/XlE0kfcfb+ZZUlaYGbPuPsrTZaZLGmXuw8ys4mS/lPSTSHCtkFr2ilJv3H3LwTI156+JGm5pC4tzEuFfXnUidoppca+lKTL3H3Hcealwucs4uNB0Qc7Kpm/s6X06IOlQ/9Log9GHyz59uVR9MHi/DmbVCN+3H2epOoTLHKjpIe90SuSuppZYXzStZ9WtDPpufsWd389+nyfGv/j92u2WNLvz1a2M6lF98/+6Mus6KP5xcNulPRQ9PlvJV1uZhaniO2ile1MembWX9J1kn5xnEWSfl9KrWpnukj6z1nEB32w1JEOfbB06H9J9MECRooJ+mBpJ66fs0lV+GmFfpI2NHm9USn4IR81Jjrc8RkzOzN0mLaIDlM8T9KiZrNSan+eoJ1Sku/P6HDNpZKqJD3v7sfdl+5eJ2mPpB7xTdl2rWinJH0iOlzzt2ZWFOeI7eEnkr4iqeE481NiX+rk7ZSSf19KjR3j58xsiZlNaWF+Sn3OIqh0+l1K6u/sptKhD5bK/S+JPlgzyf69TR/sA8m+L6UE64OlWuEnXbwuaYC7nyvpp5KeCJzntJlZJ0m/k/TP7r43dJ5YOUk7k35/unu9u4+Q1F/SaDM7K3SmWGhFO5+UVOLu50h6Xh8clUkKZna9pCp3XxI6Syy1sp1JvS+buMTdz1fjcOJ/NLNLQwcCklzSf2cflQ59sFTvf0n0wZpI6u9t+mDHSOp92URC9cFSrfCzSVLTimD/6LSU4u57jw53dPfZkrLMrGfgWKcseo7u7yT9yt1/38IiKbE/T9bOVNmfkuTuuyW9KOnqZrPe35dmlikpX9LO+KZrP8drp7vvdPcj0Ze/kHRBvLO10cWSbjCztZJ+LekjZvZIs2VSYV+etJ0psC8lSe6+KfqzStIfJI1utkhKfM4iIaTF71KqfGenQx8snfpfEn2wFPjepg8WlQL7UlLi9cFSrfDzJ0m3RK+QfZGkPe6+JXSo9mZmfY6ez2lmo9W4H5PqP300/32Slrv7j46zWNLvz9a0M9n3p5kVmFnX6PMOkq6UtKLZYn+SNCn6/JOS/uruSXVudmva2ey83BvUeE2BpOHuX3P3/u5eImmiGvfTZ5otlvT7sjXtTPZ9KUlm1tEaL2oqM+so6SpJze9UlPSfs0gYafG7lOzf2VJ69MHSof8l0QdrtkxSf2/TB/tAsu9LKTH7YEl1Vy8ze0zSeEk9zWyjpLvUeHEvuftMSbMlXStplaSDkj4XJmnbtKKdn5Q03czqJB2SNDHZ/tOrsdr7WUlvR8/XlaSvSyqWUmp/tqadyb4/CyU9ZGYZauw0/a+7P2Vmd0ta7O5/UmPn65dmtkqNF82cGC7uaWtNO79oZjeo8W4i1ZJuDZa2HaXgvmxRCu7L3pL+EP27JlPSo+7+rJlNk1LqcxZxQB8sZb6zpfTog6VD/0uiD5Zq39sfkoL7skUpuC8Trg9myff5BgAAAAAAgNZItVO9AAAAAAAAEEXhBwAAAAAAIEVR+AEAAAAAAEhRFH4AAAAAAABSFIUfAAAAAACAFEXhBwAAAAAAIEVR+AEAAAAAAEhRFH4AAAAAAABS1P8HRQaGe6L7u1gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxNXR0rI1r8w"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}